Alarm sequence rule mining extended with a time confidence parameter

Name: Alarm sequence rule mining extended with a time confidence parameter
Author: Çelebi, Ö. F., Zeydan, E., Arı, İsmail, İleri, Ö., Ergüt, S.

İsim	Alarm sequence rule mining extended with a time confidence parameter
Yazar	Çelebi, Ö. F., Zeydan, E., Arı, İsmail, İleri, Ö., Ergüt, S.
Basım Tarihi:	2014
Konu	Time confidence, Alarm correlation, Sequential rule mining, Complex event processing, Telecom network operation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Kayıt Numarası	a36c9005-07d1-41a2-9ff1-d3fca99a1315
Lokasyon	Computer Science
Tarih	2014
Notlar	AveaLabs ; TÜBİTAK
Örnek Metin	Most mobile telecommunication operators receive an overwhelming number of alarms in their networks. Network support specialists are faced with the challenge of picking the most important alarms in advance that can cause severe damages to the system or disrupt the service. A system that can discover alarm correlations and alarm rules then notify network administrators can significantly increase the efficiency of Network Operation Centers (NOC) of these mobile operators. This paper provides a new alarm correlation, rule discovery, and significant rule selection technique based on analysis of real data collected from a mobile telecom operator. We present a method based on sequential rule mining algorithm with an additional parameter called time-confidence. The time-confident rules found by this method are processed more efficiently in real-time Complex Event Processing (CEP) systems that require exact time-window values during monitoring. Furthermore, compared to traditional sequential rule mining, our proposed method adds another support dimension to eliminate meaningless rules that appear due to wrong settings of minimum support-confidence thresholds with respect to the nature of data.

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Alarm sequence rule mining extended with a time confidence parameter

Yazar Çelebi, Ö. F., Zeydan, E., Arı, İsmail, İleri, Ö., Ergüt, S.

Basım Tarihi 2014

Konu Time confidence, Alarm correlation, Sequential rule mining, Complex event processing, Telecom network operation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Kayıt Numarası a36c9005-07d1-41a2-9ff1-d3fca99a1315

Lokasyon Computer Science

Tarih 2014

Notlar AveaLabs ; TÜBİTAK

Örnek Metin Most mobile telecommunication operators receive an overwhelming number of alarms in their networks. Network support specialists are faced with the challenge of picking the most important alarms in advance that can cause severe damages to the system or disrupt the service. A system that can discover alarm correlations and alarm rules then notify network administrators can significantly increase the efficiency of Network Operation Centers (NOC) of these mobile operators. This paper provides a new alarm correlation, rule discovery, and significant rule selection technique based on analysis of real data collected from a mobile telecom operator. We present a method based on sequential rule mining algorithm with an additional parameter called time-confidence. The time-confident rules found by this method are processed more efficiently in real-time Complex Event Processing (CEP) systems that require exact time-window values during monitoring. Furthermore, compared to traditional sequential rule mining, our proposed method adds another support dimension to eliminate meaningless rules that appear due to wrong settings of minimum support-confidence thresholds with respect to the nature of data.